916 results about "Time–frequency analysis" patented technology

The invention provides a mining circuit fault self-diagnosis method and system. According to the method, current and voltage waveforms and three-dimensional vibration signals of a cable are collected, and anti-interference data are generated through self-adaptive noise reduction and time sequence synchronization; a waveform interception window is dynamically adjusted based on the correlation between the mechanical vibration intensity and the current transient rate, the wave crest slope variable quantity is extracted from the current transient segment, high-frequency harmonic energy is separated from the voltage transient segment, time-frequency analysis is carried out on the vibration signal to extract an energy sudden increase frequency point, and a mechanical damage spectrum feature set is constructed; inputting the features into a space-time correlation model, and verifying space-time consistency of current distortion and voltage abnormity to generate composite features; dynamically correcting a fault threshold based on the environmental interference factor; and finally, a short-circuit peak or open-circuit oscillation diagnosis result is output according to the association strength of the electrical and mechanical characteristics. According to the invention, accurate fault self-diagnosis of the mining cable under a complex working condition is realized.

The invention provides an equipment supervision data real-time analysis method and system, and the method comprises the steps: calculating an energy value of a frequency band corresponding to a sub-band signal, determining the energy characteristics of the surface roughness and dust deposition distribution uniformity of a blade in each sub-band in a square sum form, and generating energy distribution vectors which are arranged according to a frequency band sequence; performing time-frequency analysis on the vibration signal by using the acquired real-time rotating speed information of the gas turbine, identifying a frequency component corresponding to the eccentric distance value of the rotor and the vibration frequency offset, and determining the amplitude of the frequency component; according to the classification result and the real-time rotating speed information, the comprehensive influence index of the material density change rate and the ash deposition layer thickness difference is calculated, the fault early warning level is determined through a weighted average method, and an early warning signal is generated; and according to the change trend of the early warning signal and the blade edge wear degree, a random forest algorithm is adopted to predict a development curve of the vibration frequency offset in the future target time, and real-time equipment supervision analysis data are obtained.

The invention discloses a power distribution network line fault positioning detection system, and relates to the technical field of power distribution network fault detection. The system comprises a mixed information acquisition layer, a fault feature extraction layer, an intelligent diagnosis layer and a fault positioning layer. The mixed signal acquisition layer comprises a high-frequency transient wave recording unit, a power frequency measurement unit, a wireless pulse sensor and a distributed optical fiber temperature measurement unit; the fault feature extraction layer comprises a time-frequency analysis module, a preprocessing module and a three-dimensional feature vector module; the intelligent diagnosis layer comprises a convolutional attention network, a space-time diagram neural network and a transfer learning module; the fault positioning layer comprises a particle swarm module and a fuzzy reasoning module. According to the invention, data information of the cable is acquired through the mixed information acquisition layer, a video analysis window function is dynamically matched with signal characteristics, a time domain graph scale, a frequency domain resonance component and a space field intensity gradient are constructed, fault diagnosis and positioning are carried out by using the intelligent diagnosis layer, and the fault positioning detection efficiency of the power distribution network is improved.

A water-turbine generator set voiceprint monitoring and diagnosing method comprises the steps that a multi-channel voiceprint collecting system is adopted, a plurality of voiceprint sensors are arranged at key parts of a water-turbine generator set, and voiceprint signals at different positions are obtained; performing time-frequency analysis on the pre-processed voiceprint signal by using wavelet transform, and extracting voiceprint features to obtain a voiceprint feature vector; if the matching degree of the voiceprint feature vector and the features in the fault feature library exceeds a preset threshold value, determining the fault type and the fault degree; classifying the determined fault type and fault degree by adopting a support vector machine algorithm to obtain a fault diagnosis result; a fault diagnosis result is optimized through a deep learning network, the diagnosis precision is improved, and a final fault diagnosis report is obtained; performing association analysis on the fault diagnosis report and the equipment operation parameters to generate an equipment operation state evaluation report; according to the equipment operation state evaluation report, an equipment maintenance plan is made, and intelligent maintenance of the water-turbine generator set is achieved.

The invention provides a wind generating set fault monitoring method and system based on voiceprint recognition, and the method comprises the steps: collecting gear box voiceprint signal data in real time, recording signal fluctuation caused by gear surface wear, and obtaining an original signal data set containing a frequency spectrum high-frequency component enhancement feature; a time-frequency analysis method is adopted for the original signal data set, non-linear interference of vibration signals is recognized, multi-scale decomposition is carried out, initial wear frequency spectrum narrow-band characteristics and medium-term harmonic components are separated out, and a frequency spectrum component set is obtained; extracting characteristic parameters related to modulation depth abnormal fluctuation from the frequency spectrum component set, identifying a gear pair meshing frequency change rule, and determining a distribution mode of a tooth surface contact noise proportion; and if the feature matching result shows that the deviation between the spectrum high-frequency component diffusion distortion feature and the reference feature library exceeds a threshold value, adding the tooth surface fatigue crack noise feature into the feature library to obtain a target feature library.

The invention relates to the technical field of data processing, and discloses a double-end fault positioning method based on signal enhancement, which is used for improving the efficiency of double-end fault positioning based on signal enhancement. The method comprises the following steps: acquiring and preprocessing electrical signals at two ends of a power transmission line to obtain preprocessed synchronous electrical signals; performing adaptive enhancement processing on the preprocessed synchronous electrical signal to obtain an enhanced fault feature signal; performing time-frequency analysis and feature extraction on the enhanced fault feature signal to obtain a fault feature vector and a key parameter; performing machine learning analysis on the fault feature vectors and the key parameters to obtain a fault type identification result and initial fault position estimation; performing multi-algorithm fusion analysis on the preliminary fault position estimation to obtain an accurate fault position coordinate; and performing geographic information mapping and field verification on the accurate fault position coordinates to obtain a fault positioning result and system optimization data.

The invention provides a civil air defense engineering concealed conduit leakage positioning method based on acoustic characteristics, and belongs to the technical field of civil air defense engineering. An acoustic signal acquisition network is constructed by deploying a high-sensitivity hydrophone array, and signals are preprocessed by using a time division multiple access technology and a pulse compression technology; and carrying out time-frequency analysis by applying wavelet transform to extract acoustic features. A time reversal mirror technology is introduced to identify direct propagation and multipath reflection signals, a fluid acoustic coupling propagation equation is constructed to analyze a leakage sound source mechanism, and pipe network topological information and a sound wave speed correction function are combined to compensate a measurement error. Wherein the deep learning pipe network acoustic propagation multi-modal model is fused with a pipeline structure encoder, an acoustic feature extractor and a position prediction decoder, and high-precision leakage positioning and degree evaluation in a complex environment are realized through a three-stage pre-training strategy; the technical problem that it is difficult to accurately locate the position of a leakage point in the complex pipe network environment of civil air defense engineering is solved.

The invention discloses a metal 3D printing forming precision monitoring method and device and electronic equipment thereof, and relates to the technical field of metal 3D printing, and the method comprises the following steps: obtaining multi-modal sensor data, collecting multi-dimensional data in the metal 3D printing process based on the multi-modal sensor data, determining an optical sensor to record the molten pool form and powder distribution, and determining the metal 3D printing forming precision. The thermal infrared camera is determined to monitor temperature field distribution, the acoustic sensor is determined to collect and print medium-high frequency sound wave signals, and defect signals are identified; extracting boundary and geometric features from the optical image based on a convolutional neural network according to local preprocessing data of an edge computing device, and extracting a key temperature change area in response to thermal field gradient analysis; spectral features are extracted from the acoustic data based on time-frequency analysis. According to the invention, through synchronous acquisition of optical, thermal and acoustic multi-modal data, data fusion and defect detection are realized by using an advanced deep learning technology.

The invention discloses a real-time emotion recognition, regulation and control method and device combined with a brain-computer interface. The method comprises the following steps that S1, electroencephalogram signals of a user are collected in real time through an electroencephalogram sensor; s2, preprocessing the original electroencephalogram data; s3, processing the electroencephalogram signals in the S2 by adopting a time-frequency analysis technology, and extracting time-frequency characteristics related to the emotional state; s4, inputting the extracted time-frequency features into a trained long-short-term memory network model, performing time sequence analysis, and identifying the emotional state of the user; s5, according to the emotion state recognized in the step S4, combining historical emotion data and individual features of the user, and dynamically adjusting an emotion regulation strategy; and S6, comprehensively evaluating the emotional state of the user through a multi-modal data fusion technology. By combining the advanced time-frequency analysis technology, the long-short term memory network model and the multi-modal data fusion technology, accurate recognition and dynamic regulation and control of the emotional state of the user are achieved.

The invention provides a power grid frequency modulation power energy coordination control method and system based on a super capacitor, and relates to the technical field of power grid frequency modulation, and the method comprises the steps: carrying out the time-frequency analysis of a collected power grid frequency signal, obtaining a frequency disturbance characteristic value, and determining power control data through combining with an electrical parameter value of a super capacitor group; and performing nonlinear programming calculation based on the power control data to obtain a power distribution sequence, dividing the power distribution sequence into a quick response section, a dynamic transition section and a steady state maintenance section according to a preset threshold value, and solving a control value by adopting an augmented Lagrange equation, recursive least square operation and a Lyapunov function respectively. Obtaining a power control instruction; and inputting a power control instruction into the disturbance observer, calculating a dynamic error value, constructing a nonsingular terminal equation, and outputting a power regulation signal in combination with recursive minimum entropy calculation and a superhelix algorithm.

The invention relates to the technical field of nondestructive testing, in particular to a multi-modal fusion metal microcrack ultrasonic detection system and method, and the system comprises an ultrasonic signal acquisition module, a signal preprocessing module, a feature alignment network module, an image segmentation module, a time-frequency analysis module and a segmentation result fusion module. According to the system, three modes of electromagnetic ultrasound, laser ultrasound and phased array ultrasound are utilized to detect a metal sample, and real-time nondestructive signal data acquisition and pretreatment are realized. The feature alignment network aligns and fuses the three modal signal features, and the U-Net segmentation neural network performs submillimeter crack segmentation on the fused features. And the time-frequency analysis module extracts signal frequency and amplitude information, and evaluates the breaking strength in combination with the crack label. Finally, a segmentation result fusion module integrates crack labels and time sequence features, and whether cracks exist or not is classified through a weighted convolutional neural network. According to the system, the detection performance, the practicability and the adaptability are remarkably improved, and a new scheme is provided for precise detection of metal microcracks.

The invention provides a civil construction monitoring intelligent sensor information detection method and system under signal mismatch, and the method comprises the steps: carrying out the time-frequency analysis of temperature and strain fundamental frequency components, selecting a wavelet basis function adaptive to the concrete setting and strength increasing process, carrying out the self-adaptive threshold processing of a wavelet coefficient, removing high-frequency noise, and carrying out the detection of the temperature and strain fundamental frequency components. Low-frequency signal characteristics reflecting concrete strength increase are reserved; performing feature extraction on the denoised temperature and strain signals by adopting a singular value decomposition algorithm, detecting internal defects of the concrete through abrupt change of a singular value spectrum, and if crack and hole defects exist, causing strain signal abrupt change by stress concentration; if it is detected that defects exist in the concrete, that is, spatial heterogeneity of temperature and stress distribution exists, temperature and strain monitoring data of multi-point arrangement and a defect detection result serve as boundary conditions and constraints, and a temperature field and a stress field in the whole concrete structure are obtained through numerical simulation.

The invention relates to the field of sensor activation, in particular to a tire pressure sensor activation method and device, equipment and a storage medium. The method comprises the following steps: acquiring vehicle running state monitoring parameters and vehicle driving monitoring images in real time based on a multi-sensor array; vehicle vibration time-frequency analysis is carried out on the vehicle running state monitoring parameters, vehicle vibration situation mining is carried out, and a vehicle vibration situation curve is constructed; road object recognition is carried out on the vehicle driving monitoring image based on the vehicle vibration situation curve, real-time road condition scene structure rendering is carried out, and a real-time road condition scene model is constructed; obtaining a historical behavior log of the vehicle; carrying out multi-time-point vehicle position calculation on the vehicle historical behavior log, and carrying out dynamic vehicle movement track reconstruction so as to construct a historical vehicle dynamic track; and performing dynamic behavior feature analysis on the historical vehicle dynamic trajectory, and constructing a personalized driving portrait of the vehicle owner. According to the invention, efficient and rapid tire pressure sensor activation is realized.

The invention relates to an electric quantity abnormity intelligent monitoring method considering event waves. The method comprises the following steps: S1, collecting event wave data through a sensor and an intelligent device; s2, using a time-frequency analysis algorithm to extract time sequence features and frequency spectrum features of the electric quantity demand related event waves; s3, constructing a training set according to the extracted time sequence features and frequency spectrum features, and training an electric quantity demand motivation analysis model constructed based on machine learning and deep learning; s4, constructing an electric quantity abnormity monitoring model, calibrating a basic rule through an event wave amplitude value and frequency domain energy, learning normal waveform characteristics through an auto-encoder by the abnormity detection model, and realizing intelligent electric quantity abnormity monitoring in combination with motivation analysis; and S5, constructing a hierarchical visualization model, and graphically displaying the electric quantity change reason, the event behavior and the abnormal early warning information. According to the invention, real-time monitoring and early warning of electric quantity abnormity are realized, and challenges of transient, steady and complex non-stationary events in an electric power system can be effectively handled.

The invention relates to the technical field of intelligent identification, and discloses an intelligent identification method for a geological stress concentration area. The method comprises the steps that a plurality of vector electromagnetic induction coils are arranged in a monitoring area in a latticed mode, and original electromagnetic pulse signals containing components in the X direction, the Y direction and the Z direction are collected; performing denoising and time-frequency analysis on the original electromagnetic pulse signal to obtain a multi-dimensional data cube; processing the multi-dimensional data cube by using an integer linear programming model and an adaptive fuzzy correction algorithm to obtain a corrected electromagnetic pulse signal data set; performing decomposition and mapping relation construction on the corrected electromagnetic pulse signal data set to obtain a geological stress distribution estimation value; and constructing a geological stress concentration area three-dimensional model according to the geological stress distribution estimation value. According to the method, the stress field boundary identification precision is enhanced, and the blind area is reduced, so that high-precision stress field reconstruction under complex geological conditions is realized.

The invention discloses a mechanical transmission system fault trend prediction system based on dynamic feature recognition, and relates to the technical field of mechanical state monitoring. Comprising the following steps: synchronously acquiring a load torque signal and a lubrication state parameter signal of a transmission system and vibration acceleration signals of a plurality of measuring points through a signal acquisition module; the working condition decoupling characteristic generation module carries out time-frequency analysis on the vibration signal, calls a pre-stored load disturbance spectrum template according to a load torque signal to carry out adaptive differential processing so as to eliminate load fluctuation interference, and calls a correction rule set according to a lubrication state parameter signal to carry out form recombination on the signal so as to compensate the lubrication state influence; and finally outputting a working condition decoupling feature representing the health state of the mechanical part. And the trend prediction module calculates and obtains fault development trend and residual life estimation data through a pre-trained fault prediction model. According to the method, the dynamic characteristics representing the essential degradation of the part are effectively extracted, and the accuracy and reliability of fault trend prediction of the mechanical transmission system are improved.

The invention relates to a TCN-LSTM wind power generation capacity prediction method combining time-frequency conjoint analysis and a multi-head self-attention mechanism. The TCN-LSTM wind power generation capacity prediction method comprises the following steps: preprocessing original data of wind power generation; frequency domain features of the wind power generation data are extracted through wavelet transform, adaptive weighted fusion is carried out on the frequency domain features and time domain features, and a time-frequency conjoint analysis module is constructed; a TCN-LSTM combined network of a time convolution network TCN and a long short-term memory network LSTM is constructed; a multi-head self-attention mechanism is introduced into the TCN-LSTM combination network, and a TCN-LSTM wind energy generating capacity prediction model combining time-frequency analysis and the attention mechanism is generated; training the model by using the wind power generation data set; the trained prediction performance is evaluated; and performing wind power generation prediction by using the final wind power generation prediction model. According to the method, the accuracy of wind power generation prediction and the calculation efficiency of the model are remarkably improved, and the precision of wind power prediction is improved.

The invention relates to the technical field of active testing of circuit breakers, discloses an active testing method and system for a circuit breaker, and is used for solving the problem that an existing active testing method is difficult to realize self-adaptive adjustment of a threshold value. Time-frequency analysis is carried out on the contact speed signal to extract time domain and frequency domain features, and heat flow analysis of the thermopile array is combined to obtain a heat engine coupling feature vector; a thermo-electro-mechanical coupling dynamic graph with the sensors and the components as nodes and the reciprocal of thermal conductance as the edge weight is constructed, the structure is updated in real time, and node drift is predicted through a graph convolutional network; fusing the environment derating and the predicted drift amount to adaptively adjust a threshold value; extracting a thermoelectric coupling component of a contact speed fast change component and a heat flow refraction angle change rate, and generating a reversed polarity pulse compensation signal to suppress interference; time synchronization, closed-loop feedback of data and threshold adjustment results are realized through secondary alignment of IEEE1588 and the cloud, graph model node weight and threshold rules are dynamically updated, synchronization with equipment health degree is ensured, and accurate early warning and protection are realized.

The invention discloses a log abnormal behavior detection method and system based on periodic pattern mining and incremental learning, and relates to the technical field of system abnormality monitoring. The method comprises the steps of obtaining original log data and executing preprocessing operation; main periodic frequency components are extracted through time frequency analysis to form a periodic set, a periodic stable part sequence and a transient change part sequence are divided, and a periodic modeling mechanism and a transient modeling mechanism are used for modeling; a joint prediction model is constructed, Monte Carlo Dropout is introduced to estimate uncertainty, and Bayesian weighting is adopted to generate a prediction value; abnormity is judged through a periodic residual error, a transient residual error and an overall residual error, and an abnormal causal path is analyzed and identified in combination with transfer entropy; a memory sample driven playback and distillation mechanism is adopted to execute incremental training, and modeling structure parameters are dynamically adjusted. The method has the capabilities of periodic rule modeling, unsteady behavior expression, prediction fusion, abnormal causal identification and continuous model learning.

The invention discloses a frequency hopping signal real-time detection method and system based on FPGA + DSP. The system comprises a time frequency analysis module, a data communication module and a frequency hopping signal detection module. The method comprises the following steps: a time frequency analysis module performs analog-to-digital conversion and digital down-conversion on an intermediate frequency signal received from a radio frequency end, and performs short-time Fourier transform on a zero intermediate frequency signal by adopting a four-pipeline processing mode; the data communication module transmits the frequency spectrum information from the FPGA to the DSP through an SRIO protocol; the frequency hopping signal detection module calculates a dynamic threshold of a current frame signal according to the received frequency spectrum information, and completes preliminary detection of the signal according to a threshold value; and carrying out binarization processing and connected domain marking on the detected frequency spectrum, screening non-frequency hopping signals according to time-frequency information of a connected domain, and packaging and uploading parameter information of the detected frequency hopping signals to an upper computer. The method is simple in algorithm, low in hardware resource requirement and high in detection efficiency, and the feasibility of engineering implementation is improved.

The invention relates to the technical field of wireless communication, and particularly discloses a self-adaptive anti-interference communication method and system based on star flash-Bluetooth dual-mode, and the method comprises the steps: monitoring channel interference parameters in real time through a dual-mode radio frequency module on a communication terminal, including the out-of-band radiation power and the pulse interference duty ratio at the switching moment; instantaneous interference intensity features are extracted by using synchronous compression wavelet transform and sparse time-frequency analysis, periodic pulse interference features are identified by combining Hawkes process modeling, and a comprehensive anti-interference feature vector is constructed by fusion; the feature vectors are input into a pre-trained gradient boosting tree model for communication quality evaluation, an optimal communication mode is dynamically selected according to a scoring result, intelligent response to a complex interference environment is achieved, the stability and reliability of a communication system in a high-interference scene are improved, energy efficiency optimization and resource scheduling are considered, and the method is suitable for large-scale popularization and application. The method is suitable for industrial, medical, Internet of Things and other application scenes with strict requirements on communication quality.

The invention provides an underwater sonar target detection system and method. The underwater sonar target detection system comprises an optical fiber hydrophone array module which comprises a plurality of optical fiber hydrophone units, each unit comprises an optical fiber Bragg grating sensor and a sound pressure signal demodulation device, and the optical fiber hydrophone array module is used for collecting underwater sound wave signals in real time and converting the underwater sound wave signals into first electric signals; the sonar transmitting-receiving array module comprises a broadband sonar transmitter, a multi-beam receiver and a signal preprocessing circuit, and is used for actively transmitting a frequency modulation continuous wave signal, receiving a target reflection echo and generating a second electric signal; and the multi-mode signal fusion processing module comprises a high-speed data acquisition card, a self-adaptive noise suppression unit and a time-frequency analysis unit. The underwater sonar target detection system and method provided by the invention have the advantages of relatively high reliability, relatively high detection performance, relatively accurate cross-modal signal fusion and capability of performing intelligent target identification and tracking.

The invention relates to the technical field of evaporation process monitoring, and discloses an evaporation process monitoring method and system based on data analysis and a medium. The method comprises the following steps: acquiring data of a sensor in an evaporation cavity, and performing filtering standardization treatment to obtain process parameter data; performing time-frequency analysis and correlation calculation on the process parameter data, and extracting key parameter features; setting a dynamic threshold based on the key parameter characteristics, and calculating a deviation degree to obtain an abnormal index; and adjusting the radio frequency power and the substrate temperature in real time according to the abnormal index, and generating a control scheme. According to the method, the complex incidence relation among the multi-source sensing data can be analyzed in real time, the abnormal mode can be recognized in time, potential problems can be predicted before the abnormal condition is obviously expressed, the technological parameters are automatically adjusted through the intelligent control strategy, and therefore the technical defects that in the prior art, response of a monitoring system is lagged, and the technological parameters cannot be adjusted in real time are overcome; and the stability of the evaporation process and the product quality consistency are improved.

The invention discloses a self-adaptive noise reduction method for a positive pressure type air respirator based on airflow spectrum characteristics, and relates to the technical field of positive pressure type air respirators.The self-adaptive noise reduction method comprises the steps that a respirator air path pressure signal and an environment noise signal are synchronously collected through an airflow sensor and a microphone array; performing time-frequency analysis on the collected signals, and extracting airflow spectrum features; and establishing a mapping relation between the working state of the breathing machine and the noise spectrum based on the historical record according to the mapping relation between the working state of the breathing machine and the noise spectrum. Through real-time acquisition and analysis of airflow frequency spectrum characteristics, accurate identification of useful airflow signals and environmental noise signals, dynamic adjustment of noise reduction parameters, effective suppression of high-frequency noise and low-frequency vibration, and improvement of noise reduction effect, compared with a traditional fixed frequency band filtering or passive sound insulation material, the method can better cope with a non-stationary noise environment, and has good application prospects. The noise reduction strategy is adjusted in real time, clear auditory information is provided in a complex noise environment, and therefore the auditory perception ability of firemen is improved.

The invention relates to a power distribution network cable fault wave head identification and positioning method and system based on sheath traveling waves, and belongs to the field of power system relay protection. According to the invention, cable fault condition identification is carried out by adopting a sheath traveling wave analysis method. The method comprises the following steps: carrying out identification and fault positioning on a power distribution network cable fault wave head by combining time-frequency analysis with high-pass filtering based on traveling wave time-frequency characteristics of a cable sheath. Firstly, traveling wave measuring points are set at the head end and the tail end of a cable sheath, transient information of the head end and the tail end of the sheath during cable operation is recorded in real time, zero-sequence voltage is set as a fault judgment quantity during cable fault, high-frequency transient quantity information of the cable fault is extracted for high-pass filtering, namely interference variables such as noise and harmonic waves in traveling waves are removed, and the fault judgment quantity is calculated. For the traveling wave signal after filtering processing, each wave head information of the fault traveling wave is calibrated by adopting time-frequency analysis, fault positioning is performed through position information of a fault initial wave head and each catadioptric wave head, a criterion is formed for cable fault positioning, the criterion is simple and reliable, the coupling degree with an actual line is relatively good, and the selectivity and the sensitivity are relatively high.

The invention relates to the technical field of prediction algorithms, and discloses a high-precision active power filtering and prediction algorithm for a three-phase ammeter, which comprises the following steps: acquiring original signals of three-phase voltage and current for baseline correction, performing time-frequency analysis on non-stationary harmonic waves and noise of the original signals, and dynamically adjusting filtering parameters; voltage and current phase alignment is carried out through FIR phase shift and zero crossing point detection, a phase-locked loop is constructed by using GRU to track the frequency of a power grid, the sampling frequency is dynamically adjusted, and frequency mutation is detected; iapFFT transformation is optimized to suppress spectrum leakage, a phase error is corrected through a deep learning network, and weak harmonic detection is enhanced by using an attention mechanism; classifying harmonic waves, and adaptively calculating total active power; dimensionality reduction is carried out on historical power and environmental parameters through an auto-encoder, and future active power is predicted; low-power-consumption hardware is adapted, and training efficiency and data security are optimized; three-phase signals are processed in parallel, and FPGA storage and FFT / FIR cores are optimized for filtering processing; and dynamically adjusting parameters of the phase-locked loop.

The invention provides an underground equipment fault real-time diagnosis method and system based on edge calculation, and relates to the technical field of coal mine safety production, and the method comprises the steps: collecting multi-modal data through a distributed sensor network, extracting multi-scale time sequence features, projecting the features to a Lie group manifold space, constructing a coupling mapping relation matrix, obtaining fusion features, and carrying out the real-time diagnosis of an underground equipment fault; and constructing a causal directed acyclic graph based on a topological connection relationship and a Granger causal coefficient, executing Bayesian probabilistic reasoning, determining an execution strategy in combination with entropy similarity matching, and performing deep time-frequency analysis and causal chain verification. High-precision real-time diagnosis of equipment faults in an underground complex environment is realized, and the fault early warning accuracy is improved.

The invention discloses a centrifugal pump cavitation state recognition method based on multi-source sensor signals, which comprises the following steps: collecting the multi-source sensor signals of a centrifugal pump at different water temperatures and power frequencies to obtain multi-modal data with cavitation state labels; resampling the multi-modal signal data by using a linear interpolation method, and synchronizing and aligning the signals in a time domain; performing time-frequency analysis on multi-sensor signals by using short-time Fourier transform, performing dimensionality reduction on features through principal component analysis, and extracting one-dimensional features and two-dimensional features; a one-dimensional convolutional neural network and a two-dimensional convolutional neural network are used to carry out deep learning on signal features of different modes, and feature layer fusion is carried out on multi-dimensional features through a feature fusion strategy; classifying the fused feature vectors by using time sequence characteristics in a mode of gating a circulation unit; and adopting a weighted fusion strategy, setting a weight coefficient of each signal source according to the classification accuracy of each signal source in fault diagnosis, and inputting weighted data into a classification identification model support vector machine to obtain a final cavitation state identification result. According to the method, through the synergistic effect of the signals of the multi-source sensor, the recognition precision and robustness of the cavitation state are comprehensively improved, and the method has important economical efficiency and technical popularization value.

The invention discloses a child massage teaching auxiliary system based on artificial intelligence, and particularly relates to the technical field of massage auxiliary teaching, a Kalman filtering technology is adopted to effectively filter out large-amplitude interference in hand motion data, time-frequency analysis is carried out in combination with fast Fourier transform or wavelet transform, and frequency domain characteristics of microfibrillation signals are accurately captured; through fine micro-tremor signal analysis, noise interference is removed, the fine change of micro-tremor of the hands of a student is accurately identified, and the evaluation precision of the massage action stability is remarkably improved; and the integrated learning progress management module automatically adjusts teaching contents according to the learning progress and skill mastering conditions of the students, provides real-time feedback and forward excitation, provides excitation when the students have good performance, prompts and optimizes follow-up actions in real time when deviation occurs, helps the students to continuously improve skills, avoids error accumulation, and effectively improves the teaching effect.

The invention discloses an ultrasonic positioning microscopic imaging method and system based on joint resolution perception and vision Mangbar, and the method specifically comprises the steps: obtaining and preprocessing a plurality of modal angiography data, generating a microbubble motion sequence in combination with blood flow velocity field simulation data, and constructing a data set; inputting the data set into a mixed hierarchical feature pyramid fusion network, carrying out probability density estimation on a microbubble position based on a real-time multi-scale density field estimation algorithm, and introducing a resolution perception algorithm to enhance microbubble features to obtain a plurality of short-track images; performing dynamic feature decoupling and time-frequency analysis on the short-track image, extracting and encoding velocity component features and spatial-temporal context information of a vascular structure, and constructing a double-branch processing network based on visual Mangbar to obtain images respectively reflecting forward motion contribution and backward motion contribution, and finally generating a high-resolution blood vessel image through image fusion. The method has an important value for improving the efficiency and precision in microvascular imaging.